Learning analytics-informed teaching strategies
Enhancing interactive learning in STEM education
Bibliographic Data
| ID | 21632145 |
|---|---|
| Authors | Ying Zheng (0000-0001-5060-8581, Liaoning Normal University, corresponding author), Dexian Li (0000-0002-6994-7767, Liaoning Normal University) |
| Year | 2026 |
| Volume | 34 |
| Issue | 4 |
| Pages | 2676-2705 |
| Publication date | 2026-04-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Interactive Learning Environments (JOURNAL) |
| Journal identifiers | ISSN: 1049-4820 • E-ISSN: 1744-5191 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10494820.2025.2553113 |
| OpenAlex | W4414366540 |
| Language | EN |
| References cited | 60 |
Educational Data Mining applies data-driven techniques to analyze learning patterns, optimize teaching strategies, and improve student outcomes in STEM education. This study investigates the role of educational data mining and adaptive learning technologies in enhancing instructional methodologies, predicting student disengagement, and personalizing learning experiences. Using a mixed-methods approach, data were collected from 1,483 students and 95 teachers through random and purposive sampling. The findings indicate adaptive learning technologies significantly improve student performance by tailoring instruction to individual needs. Real-time educational data analysis enables early identification of disengagement, facilitating timely interventions. Additionally, insights into student interaction patterns inform the development of evidence-based teaching strategies that foster critical thinking and problem-solving skills. The study highlights the transformative role of educational data mining in creating immersive learning environments that enhance conceptual understanding and practical application, reducing achievement gaps among diverse student populations. This research innovatively integrates adaptive learning technologies with real-time educational analytics to provide scalable, data-driven solutions for educators. The findings emphasize the potential of educational data mining-driven instructional strategies to enhance engagement, academic success, and equitable learning opportunities in STEM education
Computer-Assisted Instruction · Educational technology · Electronic learning · Experiential learning · Interactive Learning · Interactive video · Mobile device · Teaching method · E-Learning and Knowledge Management · Educational Games and Gamification · Online Learning and Analytics
A conceptual framework for integrated STEM education
A Theoretical and Evidence-Based Conceptual Design of MetaDash
A Learning Analytics Theoretical Framework for STEM Education Virtual Reality Applications
Experience sampling methodology and technology
On data-driven decision-making for quality education
Developing a plugged-in class observation protocol in high-school blended STEM classes
Perceived benefits and barriers of a prototype early alert system to detect engagement and support ‘at-risk’ students
Expectations for supporting student engagement with learning analytics
Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education
Mining Big Data in Education
The virtual field trip
It is like a friend to me
Using thematic analysis in psychology
| Highly cited | No |
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